The present invention represents an improvement of a method for data reduction of digital video signals in a coder by vector quantization of coefficients acquired by an orthonormal transformation with a symmetrical, nearly cyclical Hadamard matrix and for reconstruction in a decoder. As described in my copending patent application Ser. No. 938,143, filed Dec. 4, 1986, an incoming video signal is divided into n blocks before the transformation, whereby the k elements of a two-dimensional block are arranged in an input vector according to a Peano curve in a first stage, and whereby a mean value coefficient and k-1 structure coefficients are formed by the transformation of the input vector, these approximately being a series development of the AC component of the input vector with pseudo-random functions.
The disclosure of my aforesaid application is incorporated by reference into this application.
Great interest in the data reduction of pictures has developed in recent years, for use for transmission of video motion pictures within new digital communications networks, for example a ISDN (Integrated Services Digital Network). Areas of employment exist, for example, in the field of so-called video conferences or of picture telphony, with transmission rates from 2 Mbits/s down to 64 kbits/s.
Such methods for data reduction of digital video signals by vector quantization have proven promising for accomplishing these tasks. Vector quantization seems superior to other, known source coding methods since it allows a data reduction close to the theoretical limit value, provided that the length m of the vectors is chosen to be of adequate size.
Vector quantization views a block of successive information parts, for example the samples of a picture, as a vector that is quantized as a unit. In contrast to a scalar quantization, vector quantization takes into consideration the statistical dependencies of the information parts on one another.
A vector quantizer seeks a k-dimensional vector from a finite set of output vectors, in what is referred to as the code book, that has the greatest similarity to the input vector. The code book index of this vector is coded with a binary code word having the length L=log.sub.2 N, in which N indicates the plurality of output vectors or the size of the code book. Differing from scalar quantization, the plurality R=L/k of bits required in order to code a vector component can be a fraction of 1.
The main hurdle in the employment of vector quantization is its complexity, which rises exponentially with R and k, i.e. a vector quantizer that works with vectors having the length k and a rate of R bits/component requires k2.sup.Rk operations for the code book search and a code book memory location of the same order of magnitude. In most applications, therefore, the block size k is limited to 16 for vector quantizing. The consequence of vectorially quantizing pictures with such small block size is that highly visible reconstruction errors appear at the block boundaries. The errors are of mainly two types, namely:
a step-like reconstruction of picture edges, caused by independent processing of the picture blocks, and
discontinuities of the gray level from block to block, whereas the gray levels of the original picture change gradually (block contouring).
In order to overcome this problem, a vector quantizing with what is referred to as an M-Hadamard transformation (MHT) is used. This type of transformation is well-suited for preventing the contouring effect.
Further advantages of the vector quantization of M-Hadamard coefficients of digital video signals is the splitting of the signal of a block of k video samples into a mean value coefficient that is proportional to the constant part of the video block, and into k-1 structure coefficients that represent the changing part or the structure of the block. For data reduction of the video block, its k-1 structure coefficients are vectorily quantized and its mean value coefficient is scalarly quantized. In order to boost the data reduction, this pattern can be cascaded, in that the mean value coefficients of k neighboring blocks are in turn combined in a vector, and are processed in the same way as set forth above.
A further characteristic of M-Hadamard coefficients is that the span w of their structure coefficients y.sub.i : ##EQU1## is linearly dependent on the standard deviation of the structure coefficients: ##EQU2## From this it follows, significantly, that the smallest span of the differences of the vector elements can be employed as a search criterion for searching a representative vector in the code book, as an equivalent measure for the smallest mean quadratic error of the vector elements that is usually employed. Since no multiplications are required for calculating the span, the computational and circuit complexity can be kept low.
The principles of M-Hadamard transformation are described in Siemens Forschungs- und Entwicklungsberichte, Vol. 13 (1984), No. 3, pp. 105-108 and from "Frequenz" 39 (1985) 7/8, pp. 226-234. The dissertation, "Transformationscodierung von digitalisierten Grautonbildern mit M-Sequenzen" by Bernard Hammer (1982) is concerned in detail with the problems of transformation coding.
The known vector quantizing methods for data reduction of video signals have the disadvantage that vectors having k elements must be processed for quantizing a block of k video samples. The mean squared error, or alternatively but with a considerably poorer result, the mean error quantity of the vector elements of the input vector and of the representative vector, are usually employed for searching a representative vector in the code book.
Greater complexity and an increased cost are required for storing the code book, as well as for a more complex computational circuit for carrying out the code book search, using a vector quantizing method.